Datasets:
license: cc-by-4.0
task_categories:
- image-to-text
language:
- da
tags:
- htr
- handwritten-text-recognition
- historical
- danish
- synthetic
- ocr
- document-ai
size_categories:
- 100K<n<1M
pretty_name: Danish HTR Synthetic (18th-Century)
Danish HTR Synthetic — 18th-Century Handwriting
📝 Blog: Segmentation is the hidden tax in historical HTR 🛠️ Generation code: github.com/AbhiPandit1/danish-htr-synthetic →
generator/
Each synthetic line image is paired with its exact ground-truth transcription.
160,000 synthetic handwritten-text-line images with perfect ground truth, for training and pretraining historical Danish handwriting recognition (HTR) models.
Historical HTR is data-starved: for most languages and eras there is too little corrected handwriting to train a recogniser directly. This dataset is a synthetic bootstrap — clean, perfectly-labelled lines that let a model learn Danish 18th-century letterforms and vocabulary before it ever sees scarce, expensive real transcriptions.
What's inside
| Lines | 160,000 |
| Era | 18th-century Danish (1700s) |
| Split | all train (by design — see Splits) |
| Label quality | Exact — the text is known by construction (no transcription noise) |
| Format | Parquet (auto-converted); source images are JPEG |
| License | CC-BY 4.0 |
Features
| Column | Type | Description |
|---|---|---|
image |
image (JPEG) | A single rendered handwriting line |
text |
string | Ground-truth transcription (3–102 characters) |
era_bucket |
string | Period label (1700s) |
split |
string | train for every line in the released set (see Splits) |
How it was generated
Each line pairs authentic-looking period handwriting with realistic capture degradation, so a model trained on it transfers to real scans:
- Scripts — historical Danish hands: gothic cursive (Kurrent), the everyday administrative hand of the era, and copperplate for formal writing. The released generator ships a documented, openly-licensed font set (Kurrent + a Schwabacher face; the OFL faces Herr Von Muellerhoff and Petit Formal Script).
- Text — real 18th-century Danish transcriptions (see Source & attribution), so the model learns the right vocabulary, spelling and letter-combinations of the era.
- Degradation — paper texture and tone, ink/stroke-weight variation, blur, noise and simulated bleed-through. In the released code every line records the font and the exact degradation parameters used, so individual factors can be isolated for diagnostic study.
The full, deterministic pipeline (rendering, degradation, splitting, manifest) is released under MIT at generator/. The result is training data with the one thing real historical corpora almost never have: exact labels at scale.
Source & attribution
The text is not invented. It is drawn from the DiEm HTR dataset (Digitalisering af Enesteministerialbøger) — the volunteer-verified transcriptions of Danish parish registers released by the Danish National Archives (Rigsarkivet) under CC-BY 4.0: RA-Data-Science/DiEm_HTR. This dataset re-uses only the text strings, with attribution, under that licence.
Provenance is verifiable: of the 36,056 unique strings here, 39% are verbatim DiEm transcription lines and 71% appear verbatim within a DiEm page, with 99% of word tokens present in the DiEm vocabulary (the remainder is the same text re-segmented at different line boundaries).
Orthography: the period form aa dominates (22% of lines); the modern letter å (official only from 1948) appears in only 8 of 160,000 lines (0.005%), where the source transcription itself uses a modernised spelling. Text is rendered as transcribed; the generator has an optional normalisation hook (e.g. å → aa), off by default.
Intended use
- Pretraining / warm-start a CTC or sequence recogniser (e.g. PyLaia, TrOCR) before fine-tuning on a small set of real corrected lines.
- Data augmentation to stabilise training on tiny real-world historical corpora.
- Ablations on how synthetic volume, degradation and script style affect downstream accuracy.
Honest note: synthetic data is a bootstrap, not a substitute for real ground truth. Always report final accuracy on a held-out set of real documents, not on synthetic data. Because the text here is drawn from DiEm, your real test set should be held out from the DiEm transcriptions to avoid text leakage.
Splits
The released set labels every line train: it was built purely as a pre-training / augmentation source, with final accuracy always measured on held-out real documents (a synthetic test split is not a meaningful target). If you want explicit, reproducible train/val/test splits, the generator produces them (assigned by hashing the text, so there is no leakage).
Quick start
from datasets import load_dataset
ds = load_dataset("abhishekjha1008/danish-htr-synthetic")
sample = ds["train"][0]
sample["image"] # PIL.Image — the handwriting line
sample["text"] # str — the ground-truth transcription
Why this exists
This dataset is part of ongoing independent research on recognising historical and handwritten text across languages and scripts — building specialist recognisers that stay faithful to the page where general vision-language models tend to hallucinate. Synthetic data like this is how you cold-start a recogniser for a language and era that does not yet have enough labelled real data.
If you use it, I'd genuinely like to hear what you built — feel free to open a discussion on the dataset.
Citation
@misc{jha2026danishhtrsynthetic,
title = {Danish HTR Synthetic: 160k Synthetic 18th-Century Danish Handwriting Lines},
author = {Jha, Abhishek},
year = {2026},
howpublished = {Hugging Face Datasets},
url = {https://huggingface.co/datasets/abhishekjha1008/danish-htr-synthetic}
}
Please also credit the source text: the DiEm HTR dataset (Rigsarkivet), CC-BY 4.0.
License & contact
Released under CC-BY 4.0 — free to use with attribution. Source text © the Danish National Archives (Rigsarkivet) / DiEm contributors, CC-BY 4.0.
Author: Abhishek Jha · GitHub · Hugging Face